Tail calibration of probabilistic forecasts
Probabilistic forecasts comprehensively describe the uncertainty in the unknown future outcome, making them essential for decision making and risk management. While several methods have been introduced to evaluate probabilistic forecasts, existing evaluation techniques are ill-suited to the evaluation of tail properties of such forecasts. However, these tail properties are often of particular interest to forecast users due to the severe impacts caused by extreme outcomes. In this work, we introduce a general notion of tail calibration for probabilistic forecasts, which allows forecasters to assess the reliability of their predictions for extreme outcomes. We study the relationships between tail calibration and standard notions of forecast calibration, and discuss connections to peaks-overthreshold models in extreme value theory. Diagnostic tools are introduced and applied in a case study on European precipitation forecasts.
Joint work with Sam Allen, Jonathan Koh, Johan Segers.
Johanna Ziegel is Professor of Statistics at ETH Zurich, Switzerland, since 2024. She is Visiting Scientist at the Heidelberg Institute for Theoretical Studies, Germany. Johanna obtained her PhD in 2009 at ETH Zurich and then held postdoctoral in Melbourne, Australia, and Heidelberg, Germany. In 2012 she joined the University of Bern, where she was promoted to Associate Professor in 2018 and to Full Professor in 2023. She has been a Council Member of the Bernoulli Society and has served as Associate Editor for several leading international journals including Mathematical Finance, Bernoulli, JASA: Theory & Methods, Journal of Financial Econometrics, and International Journal of Forecasting. She won the Credit Swiss Award for Best Teaching at the University of Bern in 2022.